Zero-shot learning via cross-modality transfer
This package contains the code for the paper Zero-Shot Learning Through Cross-Modal Transfer presented at NIPS 2013.
Please cite the code as follows:
Zero-Shot Learning Through Cross-Modal Transfer. Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Y. Ng. Advances in Neural Information Processing Systems (NIPS 2013).
To run our model and generate some of the figures presented in the paper, follow the following steps.
Preparing image data
- Download MATLAB version of dataset from http://www.cs.toronto.edu/~kriz/cifar.html.
- Merge matrices from different batches into one file -- train.mat -- and rename the test batch to test.mat and the meta file to meta.mat.
- Create folder image_data and its subdirectories, images/ and features/.
- Move all three files to image_data/images/cifar10.
- Run buildFeatures.m in buildFeatures/ to create image features for use in our model. The script will create train.mat and test.mat in image_data/features/cifar10. 
Preparing word vectors
We primarily used word vectors from , extracting 10 relevant vectors for CIFAR-10 and 96 vectors for CIFAR-100 (vectors for 4 categories from CIFAR-100 were not present in the vocabulary). You may use these directly. You can also use a different source of word vectors, saving the extracted word table for the CIFAR-10 classes under word_data/(dataset_name)/cifar10/wordTable.mat
The model can be run by executing main.m in the root directory, which will tune model parameters and generate a few graphs.
 The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization. A. Coates and A. Ng. In ICML, 2011.
 Improving Word Representations via Global Context and Multiple Word Prototypes. Eric H. Huang, Richard Socher, Christopher D. Manning and Andrew Y. Ng. Association for Computational Linguistics 2012 Conference (ACL 2012).